It has never been easier to build an AI prototype. With a capable model, a handful of documents and an afternoon, a team can create a chatbot that answers questions about company policy or summarises customer emails. Leaders see the demo, get excited — and then, months later, the project is still "in pilot".

The gap between a convincing demo and a dependable production system is where most AI investment is lost. In our experience, it usually comes down to six things.

1. No clear definition of success

"Use AI to improve customer service" is an aspiration, not a goal. Successful projects start with a measurable outcome: reduce average handling time by 25%, process 80% of invoices without human touch, cut report preparation from two days to two hours.

Fix: Agree on a baseline and a target metric before building anything. Revisit it at every milestone.

2. Data that is not ready

Prototypes often use a hand-picked set of clean documents. Production means messy, duplicated, outdated and access-controlled data from many systems.

Fix: Invest early in data pipelines — ingestion, cleaning, chunking, metadata and permissions. In retrieval-augmented generation (RAG) systems, retrieval quality determines answer quality far more than the choice of model.

3. No way to measure quality

Teams often judge a prototype by trying a few questions and deciding it "seems good". That does not scale, and it cannot detect when a change makes things worse.

Fix: Build an evaluation set of real questions or documents with known correct answers. Run it automatically whenever prompts, models or data change. Track accuracy, groundedness, latency and cost.

4. Missing guardrails

A demo can afford the occasional strange answer. A customer-facing system cannot.

Fix: Combine model-level safety with ordinary engineering controls:

  • Restrict retrieval to documents the user is allowed to see.
  • Validate outputs against schemas and business rules.
  • Detect and redact personal information.
  • Provide citations so users can verify answers.
  • Route low-confidence cases to a person.

5. Ignoring the workflow

An AI capability that lives in a separate tool will struggle to gain adoption. People do not want another window — they want help where they already work.

Fix: Embed AI into existing systems — the CRM, the ticketing tool, the ERP, the email client. Design for the moment of need, not for the novelty.

6. No plan for operations

Models change, data drifts, costs fluctuate and usage grows. Without monitoring and ownership, quality silently degrades.

Fix: Treat AI like any other production service: monitoring dashboards, alerting, cost controls, versioned prompts and models, and a named owner.

The model is perhaps 20% of a production AI system. The other 80% is data, evaluation, integration and operations.

A pragmatic path forward

  1. Pick one high-value, well-scoped use case with an engaged business owner.
  2. Build the evaluation set first, before writing prompts.
  3. Ship a supervised version to a small group of real users within weeks.
  4. Measure, improve and expand based on evidence rather than enthusiasm.

How we help

Our AI & Machine Learning team specialises in taking AI from pilot to production — with the data pipelines, evaluation suites, guardrails and monitoring that make it dependable. Cortex AI, our enterprise AI platform, provides many of these building blocks out of the box.